Adjunctive Middle Turbinectomy for Endoscopic Medial Orbital Wall - Thyroid Eye Disease Decompression

Abstract Introduction Middle turbinate resection (MTR) is commonly performed during endonasal endoscopic sinus and skull base surgery. Objective The purpose of this study was to characterize the additional orbital soft-tissue volume expansion during endoscopic medial orbital wall decompression with adjunctive MTR. Methods A retrospective review of patients who underwent endoscopic medial wall decompression with MTR was performed. The imaging software AW (GE Healthcare, Chicago, IL, USA) was used to overlay pre and postoperative orbital computed tomography (CT) images to visualize the preoperative position of the middle turbinate and the postoperative orbital soft tissue in the ethmoid bed. The imaging software Vitrea (Vital Images Inc., Minnetonka, MN, USA) was used to manually segment postoperative scans to determine the volume of orbital tissue which had filled the space previously occupied by the middle turbinate or medial to it. Results Nine orbits from 5 patients were included in this study; all patients were female with a history of hyperthyroidism. The average age was 55.6 years (range 32- 74). Of the 9 orbits, 7 (78%) had orbital soft tissue within the space of the resected middle turbinate postoperatively. The average volume of orbital tissue within or medial to this space was 0.83 +/- 0.67 cc. No patients had any postoperative complications. Conclusions In this patient cohort, adjunctive middle turbinate resection for endoscopic medial orbital wall decompression added ~ 0.83 cc of volume for orbital soft tissue after medial wall decompression. Middle turbinate resection is a useful adjunct to the orbital surgeon's armamentarium to augment the results of a medial orbital decompression for select patients.

Differentiating intradiploic orbital dermoid and epidermoid cysts utilizing clinical features and machine learning

Purpose: The purpose of this study was to characterize intradiploic dermoid and epidermoid orbital cysts to determine any differences in clinical, radiographic, or surgical features. Methods: A retrospective review was performed of patients presenting with intradiplopic dermoid or epidermoid cysts. Additionally, a complete review of the literature was performed to identify cases of intradiplopic orbital dermoid and epidermoid cysts. Data collected included age, sex, presenting symptoms, location of intradiplopic cyst, ophthalmic findings, treatment, and follow?up. Clinical features of dermoid versus epidermoid cyst were compared. Additionally, machine?learning algorithms were developed to predict histopathology based on clinical features. Results: There were 55 cases of orbital intradiploic cysts, 49 from literature review and six from our cohort. Approximately 31% had dermoid and 69% had epidermoid histopathology. Average age of patients with dermoid cysts was significantly lesser than that of patients with epidermoid cysts (23 vs. 35 years, respectively; P = 0.048). There was no difference between sex predilection, presenting symptoms, radiographic findings, or surgical treatment of dermoids and epidermoids. The majority of patients (64%) underwent craniotomy for surgical removal. Machine?learning algorithms KStar and Neural Network were able to distinguish dermoid from epidermoid with accuracies of 76.3% and 69%, respectively. Conclusion: Orbital intradiploic cysts are more commonly epidermoid in origin. Dermoid cysts presented in younger patients; however, there were no other significant differences in features including ophthalmic or radiographic findings. Despite similar features, machine learning was able to identify dermoid versus epidermoid with good accuracy. Future studies may examine the role of machine learning for clinical guidance as well as new surgical options for intervention.